Application of feature-based molecular networking in the field of algal research with special focus on mycosporine-like amino acids

Abstract

Marine red algae have been known as an excellent source for natural sunscreens and antioxidants for a long time, which outlines their potential for various medical and cosmeceutical applications. This is due to their synthesis of unique secondary metabolites to shield themselves from high levels of UV-A and -B radiation encountered in their natural habitats. In this study, a comprehensive and contemporary way for the detection, visualization, and dereplication of algal natural products with special focus on mycosporine-like amino acids (MAAs) is shown, employing HR-MS/MS metabolomics. 33 crude algal extracts were explored using ultra-high-performance liquid chromatography (UHPLC) hyphenated to orbitrap high-resolution tandem mass spectroscopy (HRMS2). Acquired raw data, subjected to pretreatment and spectral organization, could subsequently be implemented in the Global Natural Products Social (GNPS) workflow, whereby a feature based molecular network (FBMN) was created and visualized in Cytoscape. This FBMN was matched against an in-house as well as open source library on the GNPS platform and additionally enhanced by chemotaxonomic classification software and spectra of standard MAAs, as well as further information layers covering e.g. physicochemical properties, taxonomy, and fragmentation behavior. Based on the integration of the latest in silico annotation tools (SIRIUS, CANOPUS, MSNovelist) as well as already published fragmentation patterns of MAAs, structures for known compounds could be corroborated as well as those for novel substances proposed. This offers an interesting and state-of-the-art approach towards the identification and classification of known and new MAAs.